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INDONESIA
Indonesian Journal of Electrical Engineering and Computer Science
Published by Institute of Advanced Engineering and Science
ISSN : 25024752     EISSN : 25024760     DOI : -
Core Subject :
Arjuna Subject : -
Articles 64 Documents
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Search results for , issue "Vol 32, No 3: December 2023" : 64 Documents clear
Advances of vehicular ad hoc network using machine learning approach See Thian Meng; Sumendra Yogarayan; Siti Fatimah Abdul Razak; Subarmaniam Kannan; Afizan Azman
Indonesian Journal of Electrical Engineering and Computer Science Vol 32, No 3: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v32.i3.pp1426-1433

Abstract

Vehicular Ad hoc Networks (VANETs) play a crucial role in Intelligent Transportation Systems (ITS), enabling seamless communication between vehicles and other entities. VANETs provide a wide range of services, allowing vehicles to communicate with each other and with roadside infrastructure. With the increasing amount of data generated by VANETs, machine learning approaches have emerged as valuable tools to address complex challenges in this domain. This paper presents a comprehensive literature review on the application of machine learning in VANETs. The paper discusses the potential challenges and future research directions in the field, emphasizing the need for more accessible machine learning solutions for VANETs. This review emphasizes the significant role of machine learning approach in advancing the capabilities of VANETs and shaping the future of intelligent transportation systems.
Energy aware reliable routing model for sensor network enabled internet of things environment Padmini Mysuru Srikantha; Sampath Kuzhalvaimozhi; Samaresh Mallikarjun Silli; Suraj Prakash; Tanay Verma; Varun Manjunatha
Indonesian Journal of Electrical Engineering and Computer Science Vol 32, No 3: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v32.i3.pp1678-1685

Abstract

Wireless sensor networks (WSNs), which are facilitated by the internet of things (IoTs), can be difficult to improve the lifespan of the network target area. Although the hotspot issue (i.e., the cluster head closest to the base station fails quickly) is mitigated by the clustered-based routing technique, it still has an important effect on the network's lifespan and target area. However, improper distribution of load between cluster heads has been shown to negatively impact network lifespan efficiency, so even though unequal clusters have been utilized successfully to tackle the hotspot issue, further work is needed. This study provides an energy-aware reliable routing (EARR) model for resolving the hotspot as well as load balancing issues simultaneously. To extend the lifespan of the network, the EARR model effectively minimizes energy consumption by the cluster heads using enhanced multi-objective optimization parameters. Further, EARR provides improved routing optimization metrics to improve data delivery with energy efficiency, less delay, and packet loss. The results of the experiments demonstrate that the EARR model provides excellent throughput and lifespan efficiency with low delay and communication overhead.
Ataxia severity classification using enhanced feature selection and ranking optimization through machine learning model Pavithra Durganivas Seetharama; Shrishail Math
Indonesian Journal of Electrical Engineering and Computer Science Vol 32, No 3: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v32.i3.pp1605-1613

Abstract

The examination of neurological disorders and the monitoring of ataxic gait are major scientific topics that benefit from digital signal processing techniques and machine learning (ML) technologies. In this research, an ML approach is optimized with the use of Spatio-temporal data obtained from a kinect-sensor to differentiate between normal gait and ataxic. The current ML-based approaches perform very poorly because they cannot build feature-correlation among many gait characteristics. Furthermore, current ML-based techniques generate more false-positive whenever data is imbalanced in nature; especially for performing multi-label classification. This work presents a feature selection and ranking (FSR) based on extreme gradient boost (XGB) for ataxia severity classification. The FSR-XGB introduce an enhanced misclassification minimization error optimization and presents a novel feature selection and ranking to introduce feature importance using new cross-validation mechanism, both of which are aimed at solving the multi-label classification research problems. Results from experiments demonstrate that the presented FSR-XGB approach outperforms other ML-based and deep learning-based approaches.
Performance analysis of voltage source converter based high voltage direct current line under small control perturbations Reem Ahmed Mostafa; Adel Emary Salem; Ahmed Sayed Abdelhamid; Mohamed EL-Shimy Mahmoud Bekhet
Indonesian Journal of Electrical Engineering and Computer Science Vol 32, No 3: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v32.i3.pp1224-1235

Abstract

High voltage direct current (HVDC) systems provide important advantages; among them the ability to transmit enormous amounts of electrical power over great distances at low cost. As a result, planners of power systems consider it as a viable choice   for power transmission and interconnection of asynchronous networks. Depending on HVDC grids, continental/super grids have been recently constructed to promote global economic development. The study described in the paper focuses on the behavior of  a voltage sourced converter (VSC) based HVDC  transmission system comprising three arms-neutral point  clamped (NPC) converters interconnecting two asynchronous alternating current (AC)networks. In addition, the system components, and the vector control strategy of active/reactive powers and direct current (DC)bus voltage are simulated in MATLAB/Simulink under varying situations by adjusting the  controller’s  settings.  The study records and analyzes AC/DC voltages and active/reactive powers at two converter stations undervarying power and voltage conditions. The results of the study provide key performance indicators, such as settling time (tsett), steady state error (SSE), overshot/undershoot (OS%/US%), and correlation factor (CF), which demonstrate the robustness of thesystem’s control.
Patient data management using blockchain technology Vijaykumar Bidve; Kiran Kakakde; Pakiriswamy Sarasu; Shailesh Kediya; Pradip Tamkhade; Suprakash Sudarsanan Nair
Indonesian Journal of Electrical Engineering and Computer Science Vol 32, No 3: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v32.i3.pp1746-1754

Abstract

The patient data management is an essential component of healthcare systems, the secure and efficient data processing is important for the medical data. Data security, interoperability and privacy are the key requirements of data storage systems of healthcare organizations. The electronic medical records have become a key technique to maintain patient information in hospitals due to the technology revolution. Some hospital systems are also using server-based patient detail management systems, they require considerable storage to record all of the patient's medical reports, limiting scalability. They are facing difficulties, including interoperability, security and privacy worries, cyberattacks on centralized storage, and maintaining medical policy compliance simultaneously. The blockchain technology has come up with solution having decentralized and irreversible data storage. A distributed secure ledger of blockchain is the solution, enabling safe storage and retrieval of data. The proposed work yields effectively deployed smart contracts based on the system's functions, real-time patient health monitoring. The main goal of this system is to bring the whole medical data together on a single platform, employing a secured decentralized approach to store and retrieve medical information effectively.
Cybersecurity awareness among university students in Mogadishu: a comparative study Adnan Abdukadir Ahmed; Abdikadir Hussein Elmi; Abdijalil Abdullahi; Abdullahi Yahye Ahmed
Indonesian Journal of Electrical Engineering and Computer Science Vol 32, No 3: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v32.i3.pp1580-1588

Abstract

This study aimed to assess the level of cyber security awareness among graduate and undergraduate students in five universities in Mogadishu. The study used a one-way analysis of variance (ANOVA) to examine the difference in cyber security awareness levels between graduate and undergraduate students across five reputable universities. The questionnaire method was used to collect data from 250 graduate and undergraduate students from SIMAD, SIU, UNISO, Jamhuriya, and Mogadishu universities. The cross-tabulation result showed that there was a significant difference in cyber security awareness levels between the universities. Specifically, the results showed that students from SIMAD and Jamhuriya universities suffered from virus attacks, while SIU students struggled with password strength and social network misuse. Mogadishu students faced phishing and virus attacks, and UNISO students dealt with both virus attacks and password strength issues. The study recommended that universities educate their students and parents on safe internet usage and cybersecurity and monitor and secure their internet and computer services. Additionally, the authors recommended the development of cybersecurity software to help students use their data confidently and securely.
A novel machine learning based hybrid approach for breast cancer relapse prediction Ghanashyam Sahoo; Ajit Kumar Nayak; Pradyumna Kumar Tripathy; Jyotsnarani Tripathy
Indonesian Journal of Electrical Engineering and Computer Science Vol 32, No 3: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v32.i3.pp1655-1663

Abstract

The second leading cause of death for women is breast cancer, which is growing. Some cancer cells may remain in the body, so relapse is possible even if treatment begins soon after diagnosis. Since there are now many machine learning (ML) approaches to recurrence prediction in breast cancer, it is important to compare and contrast them to find the most effective one. Datasets with many features often lead to incorrect predictions because of this. In this study, correlation-based feature selection (CFS) and the flower pollination algorithm (FPA) are used to improve the quality of the wisconsin prognostic breast cancer (WPBC) and University Medical Centre, Institute of Oncology (UMCIO) breast cancer relapse datasets respectively. Data imputation, scaling, pre-process raw data. The second stage uses CFS to select discriminative features based on important feature correlations. The FPA chose the optimum attribute combination for the most precise answer. We tested the approach using 10-fold cross-validation stratification. Various trials show 84.85% and 83.92% accuracy on the WPBC and UMCIO breast cancer relapse datasets, respectively. The hybrid method performed well in feature selection, increasing the accuracy of the relapse classification for breast cancer.
Intelligent photovoltaic system to maximize the capture of solar energy Christian Ovalle Paulino; Luis Rojas Nieves; Hugo Villaverde Medrano; Ernesto Paiva Peredo
Indonesian Journal of Electrical Engineering and Computer Science Vol 32, No 3: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v32.i3.pp1557-1568

Abstract

The large consumption of electricity worldwide has an impact on the environment which can be said to alter climate change, the degradation of the ozone layer and acid rain. A house has an average daily consumption of 270kWh, this is why solar panels are very useful and can help to have a renewable energy at low costs. To create an intelligent photovoltaic system, different electronic sensors can be applied to follow the sunlight through a series of instructions in some programming software. The article proposes to prototype an intelligent photovoltaic system, based on artificial intelligence with a neural network library "propet" having a positive impact on the optimization of power generation by allowing a more accurate tracking of the sun and a greater collection of photovoltaic energy throughout the day. performing an integration between Arduino and machine learning algorithms such as artificial neural networks in prediction of time series. Different practical experiments were performed to illustrate the effectiveness of the proposed method.
Internet of things-based floor cleaning robot Abdul Hafiz Kassim; Mohamad Yusof Mat Zain; Mohd Abdul Talib Mat Yusoh; Mohd Nazrul Sidek; Raja Mohd Noorhafizi Raja Daud; Ahmad Izzat Mod Arifin; Mazratul Firdaus Mohd Zin
Indonesian Journal of Electrical Engineering and Computer Science Vol 32, No 3: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v32.i3.pp1353-1360

Abstract

Internet of t hings (IoT) based floor cleaning robot (FC - Rob) is a floor cleaning robot that uses a smartphone to assist users, primarily housewives and mothers, in completing their chores. The NodeMCU ESP8266 serves as the robot’ s “brain” and is controlled by a smartphone application for the purpose of this research. In accordance with current trends, a n iOS and Android application using Blynk has also been developed for users to control the robot’ s movements, making it the ideal solution for time - crunched individuals. FC - Rob is propelled by two d irect current (DC) motors to ensure comprehensive floor cl eaning. The results of this research are strengthened by tests conducted on battery life and two types of fabric, as well as a comparison with two types of commercially available robots. The positive findings of these tests on this robot demonstrate its effectiveness and efficiency in cleani ng houses, as well as its reasonable cost and educational value for children.
Analysis of linear congruent methods and multiplicative random number generator in computer-based test Amrullah Amrullah; Al-Khowarizmi Al-Khowarizmi; Firahmi Rizky
Indonesian Journal of Electrical Engineering and Computer Science Vol 32, No 3: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v32.i3.pp1521-1532

Abstract

This research focuses on the implementation of computer - based test exams in high schools which face the problem of not having differences in exam questions which results in weak security and validity of exam results. Therefore, a randomization method is needed to overcome this problem. The method used in random ization is linear congruent methods and multiplicative random number generator. There are 101 random questions, but only 40 questions are displayed for each student with a reference value of Xn and C, 2 will be added for each package of exam questions and to avoid question code=0, the calculation results will be added 1. The linear congruent methods (LCM) results achieve 100% accuracy, while the method The multiplicative random number generator (MRNG) only achieved 62.5% accuracy in randomizing the exam que stions. This accuracy comparison highlights the difference in the ability of the two methods to generate random permutations of test item packages. LCM randomization accuracy ensures that each student will receive a different set of test questions in a con sistent manner. However, the low accuracy of randomization using MRNG indicates a weakness in generating permutations of exam question packages. The results of this study show that the LCM method is better than the MRNG in conducting exams.

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